Whole Genome Sequencing aka “WGS” - utility in foodborne illness outbreak detection and investigations Dan Rice FDA ORA – Pacific Regional Lab Northwest.

Whole Genome Sequencingaka “WGS” - utility in

foodborne illness outbreak detection and investigations

Dan RiceFDA ORA – Pacific Regional Lab Northwest

Foodborne illness in the US We have one of the safest food supplies in world but burden of

illness still high

Estimated 1 in 6 Americans (48 million people) sick annually with foodborne illness 128,000 hospitalizations 3,000 deaths

Annual laboratory confirmed cases in US Campylobacter – 43,696 Salmonella – 41,930 E. coli O157 – 3,704 Shiga-toxin producing E. coli (STECs) – 1,579 Listeria monocytogenes – 808

JAMA June 18, 2014 311(23): 2374

We still have work to do….

http://www.cspinet.org/new/200910061.html

http://www.nextgenerationfood.com/news/risky-food-list/

The 10 Riskiest Foods

PulseNet Network of public health labs Perform standardized

protocols of PFGE on: Salmonella enterica Campylobacter ssp. E. coli O157 and other

Shiga-toxin producing E. coli (STECs)

Listeria monocytogenes Shigella spp.

Data sharing in private network

PFGE Patterns of L.

monocytogenes isolates

associated w/alfalfa sprouts

L. monocytogenes - Outbreaks and Incidence, 1978-1997

Before PulseNet (20 years)1978-19975 outbreaksMedian 69 cases/outbreak

1989: hot dogs detected as source1985: large cheese

outbreak

No. outbreaksIncidence (per million pop)

SOURCE: John Besser (CDC)

L. monocytogenes - Outbreaks and Incidence, 1978-2003

Before PulseNet (20 years)1978-19975 outbreaksMedian 69 cases/outbreak

PulseNet’s first years(6 years)1998-200314 outbreaksMedian 11 cases/outbreak

1998: PulseNetbegan

1989: hot dogs detected as source1985: large cheese

outbreak

No. outbreaksIncidence (per million pop)

SOURCE: John Besser (CDC)

L. monocytogenes - Outbreaks and Incidence, 1978-2012

Before PulseNet (20 years)1978-19975 outbreaksMedian 69 cases/outbreak

PulseNet’s first years(6 years)1998-200314 outbreaksMedian 11 cases/outbreak

Listeria Initiative & PulseNet (9 years)2004-201228 outbreaksMedian 5.5 cases/outbreak

No. outbreaksIncidence (per million pop)

SOURCE: John Besser (CDC)

Changes in technology (1983-2014)1983 First Cell Phone: Weighed 2.5lbs and could only be used for 20min before the battery died.

Use: phone calls; not widely adopted until late 1990’s/early 2000’s

Apple iPhone 6: Up to 24hr of phone talk time; up to 16 days of standby time; weighs 4.55 oz; 128GB on board storage;

Use: Phone calls, texts, web browsing, fitness tracking, photo/videos, GPS tracking, books, music, movies, games, and the list keeps growing….

Why replace PFGE with WGS?

PFGE served practical public health function but data are qualitative

Whole genome sequencing (WGS) reveals complete DNA make-up of organism, better resolution both within and between species.

Public health labs now using WGS to perform foodborne pathogen identification during foodborne illness outbreaks

Why replace PFGE with WGS?

Whole genome sequencing performs same function as PFGE but also differentiates strains of foodborne pathogens, no matter what the species

Used to determine important information such as;SerotypeVirulence attributesAntibiotic resistanceOther novel markers

Technology works on all microorganisms, ideal for laboratories that support public health

Why develop a WGS based network? Tracking and Tracing of food pathogens

Faster identification of the food involved in the outbreakGlobal travelGlobal food supply IT infrastructure exists

WGS is high resolution 3-5 million data points are collected for each isolate vs. 12 – 24

agarose gel bands

WGS analyses statistically robust Unlike PFGE patterns, WGS data analyzed in evolutionary

context. Accurate and stable genetic changes within pathogen genomes enable ID of specific common sources of outbreak strains (farms, processing plants, food types, and geographic regions).

Source Tracking is Key Application

PFGE v/s WGS

Same PFGEbut not part of the outbreak

Outbreak Isolates2-5 SNPs

SNP phylogeny for S. Bareilly strains

Is WGS a viable solution?• Cost • Increasing ease of

operation• Database longevity• Comparable times to

conventional pipelines• Sample prep

– Identical for all pathogens

• Cost savings– Resistance, subtyping,

virulence factors, more…

• New applications– tracking,

regulatory/compliance actions, historical trends, more…

2007 2008 2009 2010 2011 2012 2013$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

Cost per bacterial genome

Illumina Miseq

454

$70/genome in 2014

$40/genome in 2015 w/

Illumina NextSeq Technology

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Timeline for traditional approach to foodborne illness investigation using PFGE

Contaminated foodenters commerce

Identify contaminated food and confirm that product or

environmental samplePFGE pattern match clinical

sample pattern

Identify illnesses and get PFGE pattern from

clinical samples

Source of contaminationidentified too late to prevent most illnesses

CDC FDA/FSIS

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Timeline for foodborne illness investigationusing WGS

Contaminated food enterscommerce

Local, state and federal agencies use WGS in real-time and in parallel on clinical, food,

and environmental samples

Source of contaminationidentified early through WGS combined database queries

Averted Illnesses

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From C. Darwin's, “On the Origin of Species” - 1859

“It is obvious that the Galapagos Islands would be likely to receive colonists, whether by occasional means of transport or by formerly continuous land, from America; and the Cape de Verde Islands from Africa; and that such colonists would be liable to modification;— the principle of inheritance still betraying their original birthplace"

With WGS, we now have the ability to discern those birthplaces…

I

Detection

(species)

II

Identification

(serotype)

III

Traceback

(subtype)

Is a pathogenthere?

What kind ofpathogen is it?

Is it part of the outbreak?

Next-Generation Sequencing

qPCR/amp-based tech

Maldi-TOF MS/X-MAP

Investigating Food Contamination Events with OMICS ApproachesGetting to the information needed faster and with more precision

Health and economic impact of active WGS-based surveillance

Comparison of 2 related Salmonella contamination events

Similar facilities – broad domestic distribution

Nut butter 1 WGS not used: 42 cases and 10 hospitalizations with estimated 1,260 unreported illnesses (Fall 2012)

Nut butter 2 WGS used: – 4 confirmed cases, 1 hospitalization (Summer 2014)

WGS informed investigation prevented significant illness and hospitalizations

Current status WGS network reliable – efficient, provides very good location specificity of

outbreaks

FDA GenomeTrkr program sequenced >15,000 Salmonella and > 4,000 Listeria monocytogenes. Current rate about 1 genome per hour.

Need for increased number of well-characterized environmental (food, water, facility, etc.) sequences may outweigh need for extensive clinical isolates

Highly successful partnership between FDA, CDC and local/state public health labs on real-time tracking of FB illness outbreaks

Lessons learned WGS works – demonstrates value whenever used.

Use in tracebacks and to limit scope of food contamination events is unprecedented – numerous offshoot food safety applications exist (i.e., compliance, quality assurance, risk assessment)

Development of international open source databases promote WGS-based sentinel surveillance on a global scale

WGS more than just an “Epi-tool” - provides information on AMR, virulence, serotype, and other critical factors in one assay, including historical reference to pathogen emergence

WGS international/global ramifications to policy making (trade, commerce)

WGS for the national interest

Well established in foodborne illness systems – could extend into other

areas of infectious disease (Ebola, MERS, Chikungunya, TB, etc.)

Provides sentinel surveillance on a national/global scale for

antimicrobial resistance with real-time capacity for AMR monitoring

Ability to examine historical context and root cause analysis – ID novel

biomarkers and historical acquisition of those markers.

Potential to dramatically reduce health care costs in the US – help find

“patient zero” swiftly and accurately.

Barriers to Moving Forward Culture independent diagnostic assays – reducing

clinical isolates going into PHLs – still need an isolate

to perform WGS

Capacity building (funding and training)

Issues surrounding data and metadata release into

the public domain

Data handling – terabytes or more/isolate

What’s next?

Metagenomics………..

Questions?

Acknowledgements:- Dr. Eric Brown, FDA CFSAN- Dr. Brian Sauders, NY State Food Laboratory

Who kindly shared much of the material for this presentation

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